
"""simple docstring"""



import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


a       :						Any       		=		logging.get_logger(__name__)


def 			_UpperCamelCase						(    _A=None       ,   _A=None   )     ->	Tuple:





    """simple docstring"""

    return field(default_factory=lambda: default       ,   metadata=_A   )







@dataclass
class 		a_    :
    a   :					List[str]			     =					list_field(
        default=[]  ,    metadata={
            'help': (
                'Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version'
                ' of all available models'
            )
        }  ,    )

    a   :					List[int]			     =					list_field(
        default=[8]  ,    metadata={'help': 'List of batch sizes for which memory and time performance will be evaluated'}						)

    a   :					List[int]			     =					list_field(
        default=[8, 32, 128, 512]  ,    metadata={'help': 'List of sequence lengths for which memory and time performance will be evaluated'}  ,    )

    a   :					bool			     =					field(
        default=_UpperCAmelCase  ,    metadata={'help': 'Whether to benchmark inference of model. Inference can be disabled via --no-inference.'}  ,    )
    a   :					bool			     =					field(
        default=_UpperCAmelCase  ,    metadata={'help': 'Whether to run on available cuda devices. Cuda can be disabled via --no-cuda.'}  ,    )
    a   :					bool			     =					field(
        default=_UpperCAmelCase  ,    metadata={'help': 'Whether to run on available tpu devices. TPU can be disabled via --no-tpu.'}						)
    a   :					bool			     =					field(default=_UpperCAmelCase  ,    metadata={'help': 'Use FP16 to accelerate inference.'}						)
    a   :					bool			     =					field(default=_UpperCAmelCase  ,    metadata={'help': 'Benchmark training of model'}						)
    a   :					bool			     =					field(default=_UpperCAmelCase  ,    metadata={'help': 'Verbose memory tracing'}						)
    a   :					bool			     =					field(
        default=_UpperCAmelCase  ,    metadata={'help': 'Whether to perform speed measurements. Speed measurements can be disabled via --no-speed.'}  ,    )
    a   :					bool			     =					field(
        default=_UpperCAmelCase  ,    metadata={
            'help': 'Whether to perform memory measurements. Memory measurements can be disabled via --no-memory'
        }  ,    )
    a   :					bool			     =					field(default=_UpperCAmelCase  ,    metadata={'help': 'Trace memory line by line'}						)
    a   :					bool			     =					field(default=_UpperCAmelCase  ,    metadata={'help': 'Save result to a CSV file'}						)
    a   :					bool			     =					field(default=_UpperCAmelCase  ,    metadata={'help': 'Save all print statements in a log file'}						)
    a   :					bool			     =					field(default=_UpperCAmelCase  ,    metadata={'help': 'Whether to print environment information'}						)
    a   :					bool			     =					field(
        default=_UpperCAmelCase  ,    metadata={
            'help': (
                'Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use'
                ' multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled'
                ' for debugging / testing and on TPU.'
            )
        }  ,    )
    a   :					str			     =					field(
        default=F"""inference_time_{round(time()						)}.csv"""  ,    metadata={'help': 'CSV filename used if saving time results to csv.'}  ,    )
    a   :					str			     =					field(
        default=F"""inference_memory_{round(time()						)}.csv"""  ,    metadata={'help': 'CSV filename used if saving memory results to csv.'}  ,    )
    a   :					str			     =					field(
        default=F"""train_time_{round(time()						)}.csv"""  ,    metadata={'help': 'CSV filename used if saving time results to csv for training.'}  ,    )
    a   :					str			     =					field(
        default=F"""train_memory_{round(time()						)}.csv"""  ,    metadata={'help': 'CSV filename used if saving memory results to csv for training.'}  ,    )
    a   :					str			     =					field(
        default=F"""env_info_{round(time()						)}.csv"""  ,    metadata={'help': 'CSV filename used if saving environment information.'}  ,    )
    a   :					str			     =					field(
        default=F"""log_{round(time()						)}.csv"""  ,    metadata={'help': 'Log filename used if print statements are saved in log.'}  ,    )
    a   :					int			     =					field(default=3  ,    metadata={'help': 'Times an experiment will be run.'}						)
    a   :					bool			     =					field(
        default=_UpperCAmelCase  ,    metadata={
            'help': (
                'Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain'
                ' model weights.'
            )
        }  ,    )







    def   _snake_case       (      self      :							Optional[Any]  )		->Optional[int]:






        '''simple docstring'''






        warnings.warn(
            f"""The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"""
            """ are deprecated in general and it is advised to use external Benchmarking libraries """
            """ to benchmark Transformer models."""  ,       __UpperCamelCase  ,       )







    def   _snake_case       (      self      :							Tuple  )		->str:






        '''simple docstring'''






        return json.dumps(dataclasses.asdict(self  )  ,       indent=2  )







    @property
    def   _snake_case       (      self      :							List[Any]  )		->List[str]:






        '''simple docstring'''






        if len(self.models  ) <= 0:
            raise ValueError(
                """Please make sure you provide at least one model name / model identifier, *e.g.* `--models"""
                """ bert-base-cased` or `args.models = ['bert-base-cased']."""  )
        return self.models







    @property
    def   _snake_case       (      self      :							Optional[Any]  )		->List[str]:






        '''simple docstring'''






        if not self.multi_process:
            return False
        elif self.is_tpu:
            logger.info("""Multiprocessing is currently not possible on TPU."""  )
            return False
        else:
            return True

"""simple docstring"""



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a       :						List[Any]       		=		logging.get_logger(__name__)

a       :						str       		=		{
    '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/config.json''',
    '''umberto-commoncrawl-cased-v1''': (
        '''https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'''
    ),
    '''umberto-wikipedia-uncased-v1''': (
        '''https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'''
    ),
}




class 		a_    (			_UpperCAmelCase						):
    a   :					Any			     =					'camembert'







    def __init__(      self      :							Dict  ,       __UpperCamelCase      :							Union[str, Any]=3_05_22  ,       __UpperCamelCase      :							Tuple=7_68  ,       __UpperCamelCase      :							Optional[Any]=12  ,       __UpperCamelCase      :							int=12  ,       __UpperCamelCase      :							int=30_72  ,       __UpperCamelCase      :							int="gelu"  ,       __UpperCamelCase      :							Dict=0.1  ,       __UpperCamelCase      :							Dict=0.1  ,       __UpperCamelCase      :							List[Any]=5_12  ,       __UpperCamelCase      :							Optional[int]=2  ,       __UpperCamelCase      :							Dict=0.0_2  ,       __UpperCamelCase      :							Optional[int]=1e-12  ,       __UpperCamelCase      :							Optional[Any]=1  ,       __UpperCamelCase      :							int=0  ,       __UpperCamelCase      :							Optional[int]=2  ,       __UpperCamelCase      :							Optional[int]="absolute"  ,       __UpperCamelCase      :							int=True  ,       __UpperCamelCase      :							Dict=None  ,       **__UpperCamelCase      :							List[str]  ,       )		->Union[str, Any]:






        '''simple docstring'''






        super().__init__(pad_token_id=__UpperCamelCase  ,       bos_token_id=__UpperCamelCase  ,       eos_token_id=__UpperCamelCase  ,       **__UpperCamelCase  )

        _UpperCAmelCase        = vocab_size
        _UpperCAmelCase        = hidden_size
        _UpperCAmelCase        = num_hidden_layers
        _UpperCAmelCase        = num_attention_heads
        _UpperCAmelCase        = hidden_act
        _UpperCAmelCase        = intermediate_size
        _UpperCAmelCase        = hidden_dropout_prob
        _UpperCAmelCase        = attention_probs_dropout_prob
        _UpperCAmelCase        = max_position_embeddings
        _UpperCAmelCase        = type_vocab_size
        _UpperCAmelCase        = initializer_range
        _UpperCAmelCase        = layer_norm_eps
        _UpperCAmelCase        = position_embedding_type
        _UpperCAmelCase        = use_cache
        _UpperCAmelCase        = classifier_dropout







class 		a_    (			_UpperCAmelCase						):







    @property
    def   _snake_case       (      self      :							Optional[int]  )		->Mapping[str, Mapping[int, str]]:






        '''simple docstring'''






        if self.task == "multiple-choice":
            _UpperCAmelCase        = {0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
            _UpperCAmelCase        = {0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
            ]  )